Partial-ACO Mutation Strategies to Scale-Up Fleet Optimisation and Improve Air Quality (Best Application Paper)

Abstract

Fleet optimisation can significantly reduce the time vehicles spend traversing road networks leading to lower costs and increased capacity. Moreover, reduced road use leads to lower emissions and improved air quality. Heuristic approaches such as Ant Colony Optimisation (ACO) are effective at solving fleet optimisation but scale poorly when dealing with larger fleets. The Partial-ACO technique has substantially improved ACO’s capacity to optimise large scale vehicle fleets but there is still much scope for improvement. A method to achieve this could be to integrate simple mutation with Partial-ACO as used by other heuristic methods. This paper explores a range of mutation strategies for Partial-ACO to both improve solution quality and reduce computational costs. It is found that substituting a majority of ant simulations with simple mutation operations instead improves both the accuracy and efficiency of Partial-ACO. For real-world fleet optimisation problems of up to 45 vehicles and 437 jobs reductions in fleet traversal of approximately 50% are achieved with much less computational cost enabling larger scale problems to be tackled. Moreover, CO$_{2}$ and NO$_{\text {x}}$ emissions are cut by 3.75 Kg and 1.71 g per vehicle a day respectively improving urban air quality.